In this world, we have humans and computers. Humans learn from past experiences whereas computers need to be told what to do, so they are programmed to follow instructions. Now the question is can we get the computers to learn from experiences too? And the answer is "YES". That is what a machine learning is.
Machine Learning is an application of artificial intelligence (AI). Machine Learning is an idea to learn from examples and experience, without being explicitly programmed.
The process of learning begins with observations or data, such as examples, direct experience or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. Finding patterns in data on planet earth is possible only for human brains. The data is very massive, the time taken to compute is increased, and this is where Machine Learning comes into action, to help people with large data in minimum time.
Machine Learning Methods
Supervised Machine Learning Methods: Basically supervised learning is a learning in which we train the machine using data which is well labeled that means some data has already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that supervised learning algorithm analyses the training data (set of training examples) and produces a correct outcome from labeled data.
For Example
Suppose you are given a basket filled with different kind of fruits. Now the first step is to train the machine with all different fruits one by one like this.
- If the shape of the object is rounded and depression at the top having color RED then it will be labeled as Apple.
- If the shape of the object is a long curving cylinder having color Green Yellow then it will be labeled as Banana.
Now suppose after training the data, you have given a new separate fruit say Apple from the basket and asked to identify it.
Since the machine has already learned the things from previous data and this time have to use it wisely. It will first classify the fruit with its shape and color and would confirm the fruit name as Apple and put it in Apple category. Thus machine learns the things from training data (Basket containing fruits) and then apply the knowledge to test data (New fruit).
Unsupervised Machine Learning Algorithms: Unsupervised learning is the training of an Artificial Intelligence (AI) algorithm using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group the unsorted information according to similarities, patterns, and differences without any prior training of data.
For Instance, suppose it is given an image having both cats and dogs which have not seen ever.
This machine has no idea about the features of dogs and cats so we can't categorize it in cats and dogs. But it can categorize them according to their similarities, pattern and differences i.e, we can easily categorize the above picture into two parts. The first part may contain all pics having DOGS in it and the second part may contain all pics having CATS in it. Here you didn't learn anything before means no training data or examples.
